𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗟𝗟𝗠 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗵𝗶𝘁 𝗮 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗮𝗹𝗹: 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝘆𝗼𝘂 𝘃𝗲𝗿𝗶𝗳𝘆, 𝘁𝗵𝗲 𝘀𝗹𝗼𝘄𝗲𝗿 𝘂𝘀𝗲𝗿𝘀 𝘄𝗮𝗶𝘁.

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1️⃣ Split the work: “Fast answer” vs “Verified answer”

Fast Path (user-facing, <500ms target)

Return a provisional response after lightweight checks:

• grounding check (did it use the right context?)
• schema/format validation (can downstream systems parse it?)
• basic policy guardrails (no risky actions, no unsafe claims)

Slow Path (background workers)

Produce a verified record with heavier work:

• deeper consistency checks (contradictions, missing assumptions)
• drift/quality checks under real traffic
• full audit trail + trace artifacts (tamper-evident)

Contract boundary (the important part)

• ✅ You can read a provisional answer
• ❌ You cannot commit / execute / change state until it becomes verified

State model:
PROVISIONAL → VERIFIED → (AMENDED | REJECTED)

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2️⃣ Tracing creates “write amplification” — treat it like a data problem

If you trace every inference step, OLTP databases get punished.

So storage is separated by data “temperature”:

• Relational DB: minimal ledger + request metadata (fast queries)
• NoSQL / Event store: high-volume trace events (append-heavy)
• Object storage: large artifacts + long retention (cheap & durable)

Goal: trace I/O must not inflate p95 latency.

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3️⃣ Resilience: circuit breaker + local fallback

Providers fail. Your system shouldn’t.

A circuit breaker detects outages/latency spikes and fails over to local models.

But fallback is reduced capability mode, not “same behavior, worse quality”:

• keep policy constraints
• shrink allowed actions
• stay auditable

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What I measure (so this stays engineering, not hype)

• p95 latency (Fast Path)
• verification completion lag (Slow Path)
• mismatch rate: provisional vs verified
• trace throughput / storage cost
• circuit breaker open rate + fallback rate

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How this feels to the user (a concrete example)

Let’s say a user asks:

“Summarize last quarter’s customer escalations and propose a remediation plan.”

Fast Path returns quickly:

• a provisional summary + plan in a strict schema
• grounded on the selected context
• with safe, non-destructive actions only

The UI labels it clearly:

• PROVISIONAL (read-only)
• shows confidence + sources used
• exposes a “verification in progress” indicator

Slow Path finishes later and publishes the verified record:

• contradictions resolved (or flagged)
• missing assumptions surfaced
• audit trail + artifacts stored
• drift signals attached (if relevant)

Then the state flips:

• VERIFIED → the answer becomes actionable (can commit / execute)
• or AMENDED / REJECTED → the system either updates the response or blocks it

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The UX rule that prevents disaster

The whole point is the contract boundary:

• Reading is cheap.
• State changes are expensive.

So the system enforces:

• ✅ provisional outputs can be displayed and copied
• ❌ but cannot trigger writes, deployments, payments, tickets, data changes, or external side effects

If verification fails:

• the system does not silently “fix it”
• it amends with a diff, or rejects with reasons + artifacts

This is where most “enterprise LLM” systems break:
they let unverified text touch production.

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Where this architecture actually helps

This approach is designed for systems where:

• latency matters (human-in-the-loop, chat UX, support ops)
• but auditability matters more (regulated environments, finance, healthcare, internal controls)
• and you can’t afford “trust me bro” reasoning

In other words: fast read, slow commit.

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What I’m optimizing for (and what I refuse to optimize for)

I’m optimizing for:

• fast user-perceived latency
• strong verification guarantees
• low trace-induced p95 inflation
• clear rollback + reproducibility

I’m not optimizing for:

• “one perfect answer” at the cost of 5–15 seconds of waiting
• governance bolted on after the generation
• systems that can’t explain themselves under failure

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Next steps (if you’re building similar systems)

If you’re working on enterprise LLMs, here’s a useful exercise:

1) Write down what counts as a state change in your system

2) Make state changes impossible until verification completes

3) Treat traces as a data pipeline, not a logging feature

4) Measure mismatch rates between provisional and verified outputs

5) Add a circuit breaker + local fallback that preserves policy constraints

If you want the private/internal map (and the demo path), DM me.